{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import argparse\n",
    "import logging\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import xgboost\n",
    "from src.region_regression import Model, get_train_test\n",
    "import time\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:(400, 6)\n",
      "INFO:root:(400,)\n"
     ]
    }
   ],
   "source": [
    "logging.basicConfig(level=logging.INFO)\n",
    "x_file = 'data/pic_feature.txt'\n",
    "y_file = 'data/pic_y_feature.txt'\n",
    "model = Model(x_file, y_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use_model = SVR\n",
    "# model_para = {'C': 10, 'epsilon': 0.1}\n",
    "\n",
    "# use_model = LinearRegression\n",
    "# model_para = {'normalize':False}\n",
    "\n",
    "# use_model = DecisionTreeRegressor\n",
    "# model_para = {'min_samples_leaf': 14}\n",
    "\n",
    "use_model = xgboost.XGBRegressor\n",
    "model_para = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold_k = 5\n",
    "vis=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 1 / 5: 0.806488\n"
     ]
    },
    {
     "data": {
      "image/png": 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IMpwOoBIs5uCwmIPDYg6OSscc8WMExhhjyhYNTwTGGGPKENGJQESu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\n1+EMOgwblQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07755b9990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 2 / 5: 0.838316\n"
     ]
    },
    {
     "data": {
      "image/png": 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qqA/wvTEm2xiTD6wC/lx+J2PMUmNMvDEmvmnTpn4PUqlg89577xEbG8vdd9/Npk2bnO4T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\nY/KUiESISLqIvG91LO4SkWg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f077503dd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 3 / 5: 0.807679\n"
     ]
    },
    {
     "data": {
      "image/png": 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gzgkTF36DvxW7BWyCOBNXIv5EoKr/UtV6qtoIuAFYakkgOG6a5njv3r1cccUV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\nUyxV/QzYW2BzV2C29/fZQLe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0774df3e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 4 / 5: 0.819538\n"
     ]
    },
    {
     "data": {
      "image/png": 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D/MfJGAKhOoXCqtvkUdHNsWQYZyibUhITE9m6davH9uuvv545c+ZwyimnVHqO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\nY4yJcv8PiHMbINbEnSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0774d6fc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 5 / 5: 0.689551\n"
     ]
    },
    {
     "data": {
      "image/png": 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xSNdmnPjmC79J4Oyzz+b111/ntddeK3Hx+GhuXw+2byQcf+dU5HJlIgj1J8xw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\ninKaCJRSKsppIlBKqSj3/+euw4nsDJsDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0774e4afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg score: 0.792314\n"
     ]
    }
   ],
   "source": [
    "all_score = 0\n",
    "for i, (X_train, y_train, X_test, y_test) in enumerate(get_train_test(model.X, model.y, True, kfold_k, True)):\n",
    "#     print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "    clf = use_model(**model_para)\n",
    "    clf.fit(X_train, y_train)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    print('Score: %d / %d: %f' % (i + 1, kfold_k, score))\n",
    "    all_score += score\n",
    "    if vis:\n",
    "        y_predicted = clf.predict(X_test)\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.scatter(y_test, y_predicted)\n",
    "        ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "        ax.set_xlabel('Observed')\n",
    "        ax.set_ylabel('Predicted')\n",
    "        plt.show()\n",
    "print('avg score: %f' % (all_score / kfold_k))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f07755a8890>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fC8eOHcPtoaFAq1aa1akgt3r4nPAVFBcH5OcD/fv3x86dO1Wv77777sOePXtU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\nAgQnfMYYCxCc8BljLEBwwmeMsQDx/+laC8QBWtTnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07755a8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgboost.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "math.sqrt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
